A 6-Month Roadmap to Becoming a Data Analyst
Here's how I'd become a data analyst if I had to start over. With over a decade of experience, I have many lessons learned and would take a completely different path. Anyone—yes, you—can become a data analyst all within six months of self-study. Here's how.
The Plan: Your 6-Month Roadmap
"By failing to prepare, you are preparing to fail," says Benjamin Franklin. Without a plan, you will get lost in the data analyst journey. So, here is a six-month roadmap that I personally would use. I'd split the roadmap into several key parts: the skills, the projects, and the job applications. I would put a date on the calendar six months from now and work backward from it.
- Months 1-3: Work on skills.
- Month 4: Work on projects.
- Months 5-6: The job application process.
This timeline is very reasonable if you have about three to four hours to study every day. Everybody has different schedules and different commitments, but if that's something that you can commit to, this roadmap will work for you.
Months 1-3: Building Core Skills
Knowing what I know today, I would really just focus on learning several essential tools: Excel, SQL, and Tableau. I would learn them in that order and focus on just those for three months. I'm not including Python, cloud technology, or other BI tools because most entry-level data analyst roles will only require you to know those skills. From my personal experience as well, those were the only three tools I used for years. That is enough to get you started, and you can worry about learning the other skills after you land the job.
Excel
Excel is the first tool that I would learn because it is still the most popular BI tool used across industries. It's highly likely that in your first role as a data analyst, you'll be using a lot of Excel. You need to know how to clean, analyze, and present data using it.
You'll need about an intermediate level, where you know key functions like VLOOKUP, pivot tables, and how to create visualizations. In my roadmap, I would break this down into smaller pieces. Spend a week learning just the formulas and getting used to them. Then, spend the following week on visualizations, thinking through how you can visualize data using charts and graphs.
The best way to learn is by doing. Rather than moving on to SQL as soon as you finish Excel, take the time to practice it. For example, I would take my bank statement and try to do some analysis. You can try answering questions like, "What does the trend of my spending look like?" This would require taking all your spending and putting it on a line graph where you can see the trend of the increases and decreases of spending over time. Then, you can try bucketing your spending into categories like entertainment and food, and chart those categories over time to see how your spending changes. This is the type of analysis that a data analyst would do.
SQL
Next is SQL, which is fundamental to being a data analyst. If you have a technical assessment for your data analyst job, it will almost certainly be in SQL. Data is stored in databases, and SQL is the language you use to talk to databases to get the information you need.
SQL is a more powerful tool than Excel because it allows you to work with really large datasets and combine them to easily create different analyses. You only need to be an intermediate-level SQL user—note, I didn't say advanced. This includes functions like:
- SELECT
- WHERE
- GROUP BY
- HAVING
- JOINs
- Window functions
Some websites I personally use to learn SQL are DataCamp, Udemy, and LinkedIn Learning. DataCamp was extremely useful because it allowed me to interactively learn SQL hands-on without having to download anything. As for LinkedIn Learning, many public libraries offer a free subscription, so I was able to do a lot of my learning for free. Lastly, don't forget about the numerous good, free tutorials available online.
Tableau
Next, we move on to Tableau. Any BI visualization tool can be used in place of Tableau, such as Power BI, Looker, or Quicksight. But I personally would learn Tableau because it is the most commonly used tool that you'll see on job listings. Also, from my experience, when you start learning just one BI tool, you know about 80% of the others, so it's not as important which one you pick here.
In Tableau, you should be about an intermediate user who knows how to connect to data, add multiple data sources, and create visualizations with filters. This will get you pretty far. There's also a free version of Tableau that you can download to start practicing. You can even take the same bank statement data that you had earlier and use it to create some of the same visualizations to get practice.
Month 4: Building a Project Portfolio
This is a step that I wouldn't skip. Without any data work experience, I don't have anything to show recruiters that I know how to do data analysis. Knowing that the purpose of these projects is to use on my resume and in interviews, I would strategically use them to make myself stand out.
Here are several tips for your projects:
1. Combine Your Skills I'd create several projects that show a combination of my skills. I wouldn't create a project in just Excel and another in just SQL. I would do a combination of Excel and SQL, or SQL and Tableau. This shows the recruiter that I know which tools to use to solve which problems.
2. Solve a Problem and Tell a Story I'd create projects that have an analysis that solves a problem and tells a story. Data analysts always start their process with the problem, and it's no different for a project. A common mistake is to start with the dataset first with no plan and then work aimlessly. It's like looking at the bank statement data; without a problem statement, I would just take it and create all sorts of fancy graphs that look good but don't mean anything. It's only when I have the problem statement—"What does the trend of my spending look like?"—that I actually have a direction and can sit down and think about how I would best show this through a visualization and tell a story.
3. Use Free Data Resources Use free resources for data, such as Kaggle, Reddit, and data.gov. Even Coursera has guided projects that you can pay for.
Months 5-6: The Job Application Process
I would prepare for interviews by updating my resume, my LinkedIn, and applying for data analyst jobs. While applying for jobs, I would start practicing technical questions. Pretty much every single data analyst job that I've applied to has required a SQL technical assessment along with the general interview. I didn't know this and was completely unprepared, but you know better.
So, in month five, give yourself enough time to take some technical tests so that when it comes time for the actual interview, you'll be ready. I've personally used LeetCode and HackerRank for SQL interview questions, and they were actually pretty accurate to the ones that I've gotten in real life. Try to do the easy and medium questions, and if you can pass those, then you'll be prepared.
With this roadmap, I would start and then learn, not learn and then start. I hope this is the article that starts you on your data analyst journey.